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Drug abuse are on the rise, with many users enter the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, and they can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consist of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Networks (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1 score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% precision, 87% precision, 82% recall, 84% f1 score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and nonusers.

Pattern recognition is viral in object recognition and classification, as it can cope with the complexity of problems related to the object of the image. For example, the category of dairy cows is essential for farmers to distinguish the quality of dairy cows for motherhood. The current problem with breeders is still using the selection process manually. If the selection process using the morphology of dairy cows requires the presence of computer vision. The purpose of this study is to make it easier for dairy farmers to choose the mothers to be farmed. This work uses several processes ranging from preprocessing, segmentation, and classification of images. This study used the classification of three segmentation algorithms, namely Canny, Mask Region-Based Convolutional Neural Networks (R-CNN), and K-Means. This method aims to compare the results of the segmentation algorithm model with SVM); the model is measured with accuracy, precision, recall, and F1 Score. The expected results get the most optimal model by using multiple resistant segmentation. The most optimal model testing achieved 90.29% accuracy, 92.49% precision, 89.39% recall, and 89.95% F1 Score with a training and testing ratio of 90:10. So the most optimal segmentation method uses the K-Means algorithm with a test ratio of 90:10.